680 citations · 812 across the 12 of their papers we have counts for
3 papers · 1 filter
DynaConF: Dynamic Forecasting of Non-Stationary Time Series
Siqi Liu, Andreas Lehrmann
Deep learning has shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. H…
Continuous-time Particle Filtering for Latent Stochastic Differential Equations
Ruizhi Deng, Greg Mori, Andreas M. Lehrmann
Particle filtering is a standard Monte-Carlo approach for a wide range of sequential inference tasks. The key component of a particle filter is a set of particles with importance w…
Efficient CDF Approximations for Normalizing Flows
Chandramouli Shama Sastry, Andreas Lehrmann, Marcus Brubaker +1
Normalizing flows model a complex target distribution in terms of a bijective transform operating on a simple base distribution. As such, they enable tractable computation of a num…